Technically Speaking, November 2011

These essays discuss discrepancies in modern portfolio theory (MPT) that can be resolved by technical analysis.

In portfolio theory, asset classes’ correlations with other asset classes, their standard deviation of returns, and their expected returns are the only inputs needed to create a “global market portfolio,” which can be defined as the an optimal portfolio, whose cumulative weightings of all asset classes maximize the expected return per unit of risk. Of the three variables, two are the result of similar statistical operations. Holding an asset class in isolation, it is comprised of a standard deviation and an expected return. As such, these two variables are a corner stone of MPT. The two essays that follow present issues surrounding these variables.

Essay I: The New Liquidity Premium

The standard deviation of returns is one definition of risk for an asset class. Assuming this is a valid definition, there still remains an inadequacy in its measurement.

Rationally, and academically, one can state that the probability of an established company’s price going to zero is very low; moreover, a diversified portfolio not only renders that possibility inconsequential but is itself incapable—barring the end of civilization— of losing its entire value. The unincorporated issue is that it is not only possible, but to a degree probable, that portfolios, diversified or not, can lose significant value. Why? Trading. Trading, in any of its forms: portfolio rebalancing, strategic management, tactical management, active management, sector rotation, arbitrage, hedging, high frequency trading, value investing, speculation. Every single portfolio, even those in the realm of theory, subjects itself to changes, trades. Investing can be passive, but it cannot be immutable.

Trades result in cumulative transaction costs, cumulative trading losses, and possibly, cumulative permanent capital impairment. Examples of permanent capital impairment are leveraged investors (Bears Sterns) or short term capital losses due to speculation. The aggregate permanent capital impairment, based on all portfolios, is probably significantly high. Trading can create excess gains, but a safe assumption is that on a net basis, wealth is destroyed. Effectively, the inadequacy of the standard deviation’s measure is that since it does not account for the effects of trading on returns, it is drastically deflated. Inevitable trading losses and gains increase the standard deviation of returns. Instead of using an index, such as the Russell 3000, as a benchmark, a more accurate one would be an index of portfolios tracking the Russell 3000.

On the other hand, highly illiquid asset classes, such as private equity, real estate, and certain hedge funds, should be given an illiquidity discount! These require a holding period of several years. For these investments, because transactions and the dynamics of ever changing supply and demand are mitigated, the expected returns are usually more purely tied to their economic reality. The standard deviations do not need to be adjusted, but the expected return would decrease due to the illiquidity discount; in other words, in this paradigm, rational investors would be being willing to pay more, or accept lower returns, for illiquid investments.

In summary, the ease with which a portfolio or its components can be traded, its liquidity risk or, liquidity premium, affects both its volatility and expected returns. (Traditionally, liquidity is viewed as mitigating risk because it results in lower bid-ask spreads and the ability to trade in size.) The expected returns are affected in two ways: one is by an expected loss (or gain) in value due to trading, and the other is due to an increase in the discount rate due to the liquidity premium as defined in this essay. In the context of portfolio theory, the standard deviation of liquid asset classes should increase. The net effect of liquidity on expected returns is less clear because it is comprised of both a liquidity premium (resulting in higher required rates of return) and expected net trading losses. Altering these asset classes’ variables will result in different weightings for the global market portfolio.

Now that the effects of trading on standard deviations and returns have been established, the question remains: how can the effects be measured or predicted? The most significant impact of trading on these variables is likely to be from capital losses. Fortunately, potential capital losses would be at their highest and at their most easily detectable simultaneously: during sentiment extremes. At such extremes, the “majority” is depicted as being largely invested or not-invested in an asset class whose price is about to undergo a profound trend reversal. The potential capital losses resulting in the trend change would be compounded for those who bought at overbought levels or sold at oversold levels.

One could observe such behavior among individual and institutional investors, who have discretion regarding in what asset classes their funds are invested. The following weekly chart (courtesy of stockcharts.com) depicts the ratio of an Aggregate Bond Index to the Russell 3000 Index, plotted against the 10 week moving average on the CBOE Equity Put-Call Ratio, a well established sentiment indicator.

Note that the relative strength line has a high correlation with the put-call ratio, indicating that at extremes in sentiment, the outperforming asset class may begin underperforming.

As you can see, the technical tools of relative strength and sentiment analysis can help predict potential capital losses resulting from a trend reversal of a favored asset class. I propose a “Permanent Capital Impairment Index” be devised, based on such studies, which can be used to adjust expected returns and standard deviations.

Essay II: The Equity Risk Premium

When determining the value of a company using fundamental analysis, the inputs required are expected cash flows, expected growth, and a discount rate, which can be broken down into a T-bill’s yield plus and an equity risk premium (ERP). The discount rate, all else equal, has an enormous effect on valuation. Of the five inputs stated above, all are objectively measurable, except for the equity risk premium.

The one method for defining and forecasting an asset class’ risk premium is using modern portfolio theory itself. For example, the ERP is neatly expressed in a formula: Equity risk premium equals the global portfolio’s risk premium divided by its standard deviation, multiplied by the correlation between equity and the global portfolio returns, again multiplied by the standard deviation of equity returns. It’s a mouthful, but the equation itself is not important. What is important, vital even, is that this concept contains
a subtle, circular logic:

The risk premium of an asset class comprises that asset’s expected return. The risk premium of an asset class is derived from a global market risk premium, as stated in the equation above. The global market risk premium is derived from asset classes’ expected returns (and standard deviations).

This circular logic is not obvious because of the intricacies and various facets of modern portfolio theory. Regardless, the equity risk premium remains in a nebulous state.

If you dig deep enough, you discover that there is no consensus on how to determine this abstract ERP. Except for the method discussed above, at best, one can define all the other variables and solve for an implied ERP with simple algebra. Evidence for the difficulty this concept presents is a 95 page paper titled, Equity Risk Premiums (ERP): Determinants, Estimation, and Implications, written by Aswath Damodaran, professor of finance at Stern School of Business. In it, he states, “determinants of equity risk premiums [include] macroeconomic volatility, investor risk aversion and behavioral components” (p. 86). Although the author manages to exclude technical terms in his explanation, no doubt, investor risk aversion and behavior have equivalent principles in the technical realm.

The frequency and rate at which market prices fluctuate on a daily, weekly, and monthly basis, cannot be accounted for as consensus adjustments to cash flow or growth expectations; these long term variables simply don’t move that quickly or greatly, and daily snippets of information are not material enough. (Even on a long term basis, changes in PEs have accounted more for stock market fluctuations than changes in earnings.) The fundamental analyst must resort to a changing ERP. But what is more likely, that a sharp increase in volatility is due to the equity risk premium undergoing nuclear fission, or because of panic selling? Panic selling is irrefutably observable and common sense, yet only acknowledged by technical analysis. If the ERP were more clearly definable, it could be more accurately estimated. If that were the case, the range of possible fair values of a stock, or stocks in general, would narrow and volatility would decrease. However, prices do fluctuate drastically over all time frames, and those fluctuations are predicted using technical analysis, regardless of whether it is a viable theory. (The argument that PE ratios are technical in nature has been posed before; it is simply a loose version of the same argument that this essay has applied to the ERP. More accurately stated, PE ratios are not technical, rather, an element of the PE ratio, the ERP, is technical.)

Ironically, a yearly plot of the implied ERP spanning fifty years, below, presents a clear technical picture: acceleration due to a “trading range” break out; a very gratifying double top in 1979—shortly before the 80s boom—and 2008, from which prices more than doubled. The dot-com boom had an unprecedentedly low ERP of 2%, meaning that investors only required a yearly return of 2% over treasuries to take on the risk of owning equities.

From Equity Risk Premiums (ERP): Determinants, Estimation, and Implications, page 64. The technical drawings are my own.

Just as with the CBEO Volatility Index, charting the implied ERP would benefit the financial community. A weekly basis is probably the most granular the data should be before it obscures into noise.

To summarize, the equity risk premium component of expected returns, a crucial element of MPT, cannot satisfactorily and comprehensively be defined by its own theory or by fundamental analysis. Technical analysis thereby stands as either a theoretical substitute or amended to the theory.

The Common Conclusion

Some of the assumptions of modern portfolio theory are so drastic that they can’t even be challenged. They rely fully on the inherent protection granted to assumptions. To list a few, to which I have added exclamation marks:

• All asset classes are infinitely divisible!
• All investors are rational!
• All investors define risk and return the exact same way!!
• All investors share the exact same expectations for what the standard deviations and returns for each asset class will be!!!

The two essays presented here have challenged the basic, seemingly objective variables of modern portfolio theory, revealing subtler deficiencies. The first essay concluded that liquidity should alter the standard deviation and expected returns because of trading effects. These effects can be best predicted by sentiment based technical analysis. The second essay argued that changes in an essential component of expected returns, the equity risk premium, are best explained by the supply and demand dynamics that technical analysis studies.

Modern portfolio theory serves a great purpose, with which no current theory can contend. It standardizes investing. Investors with no interest or desire to invest based on fundamental or technical analysis can do so with a method that can, to a degree, protect their capital. Financial institutions can easily perform this service, and nearly anyone can learn and understand how to apply MPT to client portfolios. Without such standardization, investing would be chaotic, and much more unnecessary wealth destruction would occur. The value of the ideas presented in this essay is that they can be applied to improve portfolio theory through better estimates of standard deviations and expected returns. Thereby, modern portfolio theory becomes a platform that can integrate technical and fundamental analysis.

Charles Dow was born in Sterling, Connecticut, on November 6, 1851. He was first and foremost a newspaperman. When he was 21 years old, he apprenticed for six years at the Springfield, Massachusetts Springfield Republican newspaper. He then moved to Rhode Island. In 1879, while working for The Providence Journal, he was sent to Leadville, Colorado, to write about the “Silver Rush”. His five “Leadville Letters” or articles marked his first career achievement. Shortly after returning to Rhode Island, Dow moved to New York City. 

In 1882, with Edward Jones, he founded Dow Jones & Company. Shortly thereafter, he began publishing a two-page newspaper called The Customer’s Afternoon Letter, a compilation of current financial news and stock prices.

Dow became a member of the New York Stock Exchange in 1886. The New York Times featured a story on his initiation in January 1, 1886 (Figure 1).

Figure 1: Dow Joins the NYSE

On July 8, 1889, with Dow as the founder and Editor, The Wall Street Journal, the successor to The Customer’s Afternoon Letter, began publication. A yearly subscription was $5.00, and the paper had four pages of financial news and statistics. Let’s not downplay the founding of The Wall Street Journal, a daily compilation of market information that brought the financial markets closer to the public. The WSJ brought Wall Street closer to Main Street.

William Peter Hamilton wrote of Dow during those early years: “His perfect integrity and good sense commanded the confidence of every man in Wall Street, at a time when there were few efficient newspaper men covering the financial section, and of these – fewer with any depth of knowledge of finance.”

In the field of technical analysis, Dow is recognized for two accomplishments: his editorials in The Wall Street Journal which became the germinating seed for Dow Theory, and the creation of the Dow Jones Industrial and Rails Averages (later renamed the Transportation Index).

Some twelve years after founding The Wall Street Journal, Dow began to write editorials on the actions of the stock market. These were editorials, not how-to-trade articles. Many of the editorials were framed as answers to questions from “correspondents,” or the readers. The editorials which in 1903 Samuel A. Nelson compiled into a book (The A B C of Stock Speculation), and while doing so, coined the term “Dow’s Theory,” were published mostly during 1901 and the first half of 1902.

In subsequent years, those editorials became the basis of stock market theories and principles. Hamilton, Dow’s successor Editor at The Wall Street Journal, Robert Rhea, Dr. George W. Bishop, Jr., E. George Schaefer, and Richard Russell developed what has become known as Dow Theory.

It was in The Customer’s Afternoon Letter that Dow first published his industrial index on July 3, 1884. The first index had 11 stocks, 9 railroads and 2 industrials. A year later, the index consisted of 12 railroads and 2 industrials. In 1897, it was split into a 12 stock industrial index and a 20 stock rails index. In 1916, the industrial index was increased to 20 stocks, and in 1928 to 30 stocks. Although General Electric was in the first index, it was dropped in 1898 for a short time, and subsequently was restored to the index, making GE the sole survivor of the original industrial index.

The index was price-weighted and basic by modern standards, but the idea of measuring the general action of stock market prices was original. Dow focused on the general trend of the market. According to Dow, there is a general trend, and it can be measured and visualized. Figure 2 includes several excerpts of Dow’s editorials, showing the wide range of commentary Dow made, and further showing the basis of some well-established market concepts.

CHARLES H. DOW ON TRADING STOCKS

• The outside trader should not attempt to deal in more than two or three stocks at a time.
• He should keep with his price movement a record of the volume of transactions.
• Discover what a stock can be expected to be worth three months hence.
• The prudent trader, however, would take only part of his line. He would buy perhaps one-half of the stock he wanted and then give an order to buy the rest later.
• It is impossible to tell in advance the length of any primary movement, but the further it goes, the greater the reaction when it comes.
• Either cut losses short, or take an investment position.
• Many people seem to think that the change in prices in any one day is complete in itself and bears no relation to the larger movements which may be under way. This is not so. Nothing is more certain than that the market has three well defined movements which fit into each other.
• If people with either large or small capital would look upon trading in stocks as an attempt to get 12 per cent per annum on their money instead of 50 per cent weekly, they would come out a good deal better in the long run.

Figure 2: Excerpts of Dow’s Editorials

Charles H. Dow died in December 1902, at the age of 51 (Figure 3). That same year, Clarence W. Barron bought Dow Jones & Company.

Figure 3: Dow’s Obituary

For the third year, the Market Technicians Association Educational
Foundation (www.mtaef.org) “Take an Analyst to Lunch or Dinner” was a
success! Over the summer, a global collection of top-shelf analysts and traders was assembled. This year volunteers ranged from the Far East (Marc Faber) to Switzerland (Felix Zulauf) to London (Tony Plummer), and an “A to Z list” here in the United States from the east to west coasts.

Some bids came in early and ramped up quickly and, for others, the bidding came in late but furious. When the 10-day auction window closed, one winner donated $3,310 to take Dan Zanger to lunch or dinner! Mr. Zanger has since agreed to a lunch or dinner with an unsuccessful bidder who kindly matched the $3,310 bid! You can meet him on November 8th at the MTAEF Annual Fundraising event at Baruch College in New York City (http://go.mta.org/157).

Marc Faber was also very popular with a $1,225 winning bid and two other interested parties are willing to match that bid – hopefully Marc will be able to accommodate them when he comes to the States. Felix Zulauf was recently in New York City for a conference and seems to have had a great time with his bidder just a week ago!

Followers of the Elliott Wave Principle opened their checkbooks for Jeff Kennedy, who raised $1,240 for the Foundation as he will be going out to lunch twice. I will be going out with Jeffrey Hirsch soon and he is donating autographed copies of the 2012 Stock Trader’s Almanac to the silent auction at our next event on November 8th at Baruch.

The 2012 Auction? Some very visible people in our industry declined to participate this year because of other commitments but they will be the first ones who I will ask next year, so open an account on eBay and get ready!

This article was originally published in 2007, and was based on research involving 7,050,517 trades from Jan 1, 1995 to June 30, 2006. We applied a price and liquidity filter that required all stocks be priced above $5 and have a 100-day moving average of volume greater than 250,000 shares. This research has been updated through 2010 and currently involves 11,022,431 simulated trades.

We consider the Relative Strength Index (RSI) to be one of the best indicators available. There are a number of books and articles written about RSI, how to use it, and the value it provides in predicting the short-term direction of stock prices. Unfortunately, few, if any, of these claims are backed up by statistical studies. This is very surprising considering how popular RSI is as an indicator and how many traders rely upon it.

Most traders use the 14-period RSI, but our studies have shown that statistically, there is no edge going out that far. However, when you shorten the timeframe you start seeing some very impressive results. Our research shows that the most robust and consistent results are obtained by using a 2-period RSI and we have built many successful trading systems that incorporate the 2-period RSI.

Before getting to the actual strategy, here’s a little background on the RSI and how it’s calculated.

Relative Strength Index

The Relative Strength Index (RSI) was developed by J. Welles Wilder in the 1970’s. It is a very useful and popular momentum oscillator that compares the magnitude of a stock’s recent gains to the magnitude of its recent losses.

A simple formula (see below) converts the price action into a number between 1 and 100. The most common use of this indicator is to gauge overbought and oversold conditions – put simply, the higher the number the more overbought the stock is, and the lower the number the more oversold the stock is.

As mentioned above, the default/most common setting for RSI is 14-periods. You can change this default setting in most charting packages very easily but if you are unsure how to do this please contact your software vendor.

2-Period RSI

We looked at over eleven million trades from 1/1/95 to 12/30/10. The table below shows the average percentage gain/loss for all stocks during our test period** over a 1-day, 2-day, and 1-week (5-days) period. These numbers represent the benchmark which we use for comparisons.

We then quantified overbought and oversold conditions as measured by the 2-period RSI reading being above 90 (overbought) and below 10 (oversold). In other words we looked at all stocks with a 2-period RSI reading above 90, 95, 98 and 99, which we consider overbought; and all stocks with a 2-period RSI reading below 10, 5, 2 and 1, which we consider oversold. We then compared these results to the benchmarks, here’s what we found:

Oversold

The average returns of stocks with a 2-period RSI reading below 10 outperformed the benchmark
1-day (+0.08%), 2-days (+0.20%), and 1-week later (+0.49%).
The average returns of stocks with a 2-period RSI reading below 5 significantly outperformed the benchmark
1-day (+0.14%), 2-days (+0.32%), and 1-week later (+0.61%).
The average returns of stocks with a 2-period RSI reading below 2 significantly outperformed the benchmark
1-day (+0.24%), 2-days (+0.48%), and 1-week later (+0.75%).
The average returns of stocks with a 2-period RSI reading below 1 significantly outperformed the benchmark
1-day (+0.30), 2-days (+0.62%), and 1-week later (+0.84%).

When looking at these results, it is important to understand that the performance improved dramatically each step of the way. The average returns of stocks with a 2-period RSI reading below 2 were much greater than those stocks with a 2-period RSI reading below 5, etc.

This means traders should look to build strategies around stocks with a 2-period RSI reading below 10.

Overbought

The average returns of stocks with a 2-period RSI reading above 90 underperformed the benchmark
2-days (0.01%), and 1-week later (0.02%).
The average returns of stocks with a 2-period RSI reading above 95 underperformed the benchmark
and were negative 2-days later (-0.05%), and 1-week later (-0.05%).
The average returns of stocks with a 2-period RSI reading above 98 were negative 1-day (-0.04%),
2-days (-0.12%), and 1-week later (-0.14%).
The average returns of stocks with a 2-period RSI reading above 99 were negative 1-day (-0.07%),
2-days (-0.19%), and 1-week later (-0.21%).

When looking at these results, it is important to understand that the performance deteriorated dramatically each step of the way. The average returns of stocks with a 2-period RSI reading above 98 were significantly lower than those stocks with a 2-period RSI reading above 95, etc.

This means stocks with a 2-period RSI reading above 90 should be avoided. Aggressive traders may look to build short selling strategies around these stocks.

As you can see, on average, stocks with a 2-period RSI below 2 show a positive return over the next week (+0.75%). Also shown is that, on average, stocks with a 2-period RSI above 98 show a negative return over the next week. And, just as the other articles in this series have shown (they are available on tradingmarkets.com, where you can view archives), these results can be improved even further by filtering stocks trading above/below the 200-day moving average and combining the 2-period RSI with Power Ratings.

Recent Examples

Chart 1 (below) is an example of a stock that recently had a 2-period RSI reading below 2:

Chart 1

Chart 2 (below) is an example of a stock that recently had a 2-period RSI reading above 98:

Chart 2

Our research shows that the Relative Strength Index is indeed an excellent indicator, when used correctly. We say “when used correctly” because our research shows that it is possible to catch short-term moves in stocks using the 2-period RSI, but it also shows that when using the “traditional” 14-period RSI there is little/no value to this indicator. This statement cuts to the very essence of what TradingMarkets represents – we base our trading decisions on quantitative research. This philosophy allows us to objectively assess whether a trade offers a good risk/reward opportunity and what might happen in the future.

This research we’re presenting here is just the tip of the iceberg using the 2-period RSI. For example, greater results can be found by looking for multiple day readings under 10, 5 or 2. And, even greater results can be achieved by combining the readings with other factors such as buying a low level RSI stock if it trades 1-3% lower intraday. As time passes we’ll share some of these research findings, along with handful of trading strategies with you.

Maverick is the charting platform available from TraderMade. This is an extremely versatile package, with full charting and trading functionality combined with a comprehensive news and analysis service known as InterpreTA.

TraderMade has been providing charting solutions since 1984. It is a company that supports the highest professional standards in technical analysis. Most positions at the company require that the individual will be expected to complete a Diploma in Technical Analysis from the STA (or IFTA or MTA), and they are expected to begin the program shortly after starting work with TraderMade.

The commitment to technical analysis is obvious in their products. The software offers the typical suite of technical analysis tools. All indicators and drawing tools are easily accessible via menus or shortcuts. An example screen shot is shown below, with Fibonacci levels, moving averages, trend lines and annotations added to the chart.

Among the less well known indicators offered in Maverick are Marney Indicators, a set of indicators designed as a proxy for volume in foreign exchange markets. TraderMade is an expert in the FX markets, collating data from both Market Data Providers and Liquidity Providers. Its price histories offer reliable and detailed data series beginning with dates in the 1970’s for many currency pairs. The company notes that, “Each incoming data point is subject to a 5-step cleaning and validating process, which
provides the cleanest and most accurate available in the marketplace.”

Data for commodities is also available, as is intraday stock data for XETRA, Frankfurt, Zurich, Milan and the FTSE 350 from the London Stock Exchanges. End of Day stock data is available for 3,500 shares from the 32 main worldwide stock exchanges.

A complete range of exchange indices are provided and the user also has the ability to design their baskets or indexes by combining instruments to create trade-weighted indexes or representations of their own portfolios or benchmarks. These user-defined indexes can then be plotted in standard charts or used as overlays. An example is featured below:

Maverick is adding partners to allow for direct trading from the software platform. For now, it offers real time alerts, even when the user is away from their trading screen via email or text. The alerts can be set for a variety of patterns or keyed to user-drawn trend lines. An alert generated by trend line breaks is a feature that is not commonly found in software packages.

InterpreTA is a robust intraday news and analysis service for the key FX and commodities markets. Continuously updated research and reviews are provided for each market, along with assessments of their recent performances. Expert analysts provide technical predictions of price action for the short (1-2 days) and medium term (3-10 days).

The InterpreTA service is available bundled with Maverick and it is also in Thomson Reuters platforms.

This analysis covers 43 FX and commodity markets. It includes annotated charts with real-time data provided alongside the commentary. Key support and resistance levels are highlighted and alerts can be delivered through InterpreTA or Twitter.

Maverick and InterpreTA is a complete package for traders and market analysts. Having news available with customizable charts is an indispensible too for anyone creating reports on the market. Alerts are an indispensible feature for the independent trader. Maverick truly offers something for everyone involved in the markets and should be considered by anyone researching advanced charting software.

More information on TraderMade and Maverick is available at http://go.mta.org/158. InterpreTA is detailed at http://go.mta.org/152

This book, Dave Landry’s third, is a how-to guide for traders of all skill levels. It provides detailed and precise instructions on how to trade and analyze the markets, including enough information so that someone with no experience could begin trading after reading the book.

Unusual for a book that meets the needs of beginning traders, “The Layman’s Guide to Trading Stocks” also includes descriptions of techniques that the most experienced traders and analysts can benefit from. The author walks through his personal work routine, a valuable resource for professionals. Many market analysts have developed their own work processes without knowing how other professionals complete their work, and a detailed glimpse into another successful professional’s day can be helpful. You may not decide to adopt any of the trading or analytical techniques that are highlighted in the book, but will still benefit from thinking about whether your approach is as disciplined as it should be.

It is also important to note that the book is not just about trading. Analysis is an equally important part of the book and Dave explains how to use charts to develop a detailed analysis. The patterns will be just as useful for someone developing a market outlook report as they will be for those looking for a short-term trading idea.

The first part of the book is a review of the basics, and even the most experienced trader can benefit from that. Dave writes on the importance of this in the introduction, “When you find yourself in a hole, the best thing to do is head back to the basics.” Traders and analysts often forget this simple idea, but professional athletes always endure preseason training to brush up on the basics. That lesson has not been lost on Dave who seems to have spent considerable time thinking about the fundamentals that make a good trader and investor, and he offers an excellent overview of the investment basics.

From here, the book is directed at long-term investors as well as short-term traders.

In the first chapter, long-term investors are confronted with Wall Street myths. Technical analysts understand that markets go down as well as up, and the broad market can trade within a range for decades. Many average investors are fooled by the myth that “The market always goes up longer-term.” Dave’s charts help the layman understand this isn’t necessarily true, even when the long term encompasses more than twenty years. He does this by showing that the market took nearly 23 years to surpass its 1929 peak in 1952, in one example.

Other Wall Street myths are explained and illustrated. Then several Wall Street truths are offered, including “If you are smart, it is going to take a lot longer.” Here Dave quotes trading wizard William Eckhardt who said, “I haven’t seen much correlation between good trading and intelligence…Many outstanding intelligent people are horrible traders. Average intelligence is enough.” This explains the need for a disciplined approach to analyzing and trading the markets. To help even the long-term investor outsmart the markets, Dave believes technical analysis can help.

The basics include chart construction and price analysis. Trading means taking advantage of price moves, and price movements really are revealed in simple bar charts. Dave builds a complete trading philosophy from this powerful concept that is often ignored by analysts, layman and professional, who are looking for the Holy Grail with complex indicators.

Trend identification is important in both the long-term and the short-term. The idea of trends has been complicated by many analysts, but Dave presents a simple image to explain it.

He recommends drawing arrows on charts to identify the trend’s direction. And he notes that a trend is a persistent price moves – an uptrend is actually a series of higher highs and higher lows, a pattern that can easily be seen on a price chart.

In addition to arrows that show the general market direction, Dave explains the visual importance of gaps on a chart. Strong closes near the day’s high are another visual trading tool, and he advises traders to enter on a pullback. Pullbacks are explained in detail and examples show the novice trader how they look on a chart. This is a point the layman trying to bottom fish can easily miss, and Dave explains the importance of buying only during an uptrend.

Moving averages are another trading tool that is featured in “The Layman’s Guide,” which will not surprise those that are familiar with Dave’s previous work. For those unfamiliar with his work, an article he wrote at tradingmarkets.com (Swing Traders: Find More Reliable Tops and Bottoms Using Bow Ties) in 2003 highlights the bow tie pattern, http://go.mta.org/145

In this book, Dave acknowledges that some will think of his work as simplistic. He notes that Leonardo DaVinci said “Simplicity is the ultimate sophistication.” This book contains examples of many simple price patterns that can work as a disciplined trading strategy. One is the Trend Knockout to “identify strong trends from which the weak hands have already been knocked out.” Specifically, the buy rules are:

• The stock should be in a strong and persistent uptrend.
• A pullback shows that weak money has exited. The pullback should drive the price down below at least the two prior lows and have a larger than average range. This is the knockout.
• Go long above the high of the knockout bar.

The book includes a number of examples:

Other patterns, simple yet effective, are shown. From there, Dave turns his attention to stops, where he notices the Goldilocks problem. Stops are often too tight which cause whipsaws or too loose which lead to large losses. Just right stops are those that are far enough away to allow you to hold the trade through normal market action but tight enough to avoid holding a position after the trend has reversed. Applying his fundamental principles of simplicity and the importance of price, stop placement using charts is illustrated.

There will be many trade setups, but not each trade needs to be taken. Stops can be used to decide which signals to use for trade entry – if the stop would be too tight or risk would be too great, the trade doesn’t need to be taken. This is a simple yet effective trading filter. 

Trailing stops can be used to manage winning trades. Stops can help avoid many of the problems that traders face, especially those related to a lack of discipline. Dave presents a section on trader psychology. This is an example of a basic trading idea that can be overlooked by experienced traders who can be overcome by emotion at times and overtrade or take an excessively large position. Consistent with his writing, he reduces psychology to simple principles, the three Ms:

1. Money management
2. Methodology
3. Mind

Detailed explanations of each M help traders of all experience levels understand what they need to do to succeed.

Section I, the part of the book intended as a review of the basics, ends with checklists that can be used as a complete trade plan. In the next section, that plan is expanded to show Dave’s daily routine including a scan he uses to identify trading candidates and details on his analytical process. Advanced trading patterns, including the bow tie pattern referenced above,
are explained.

The last chapter is a complete review of a trade, with numerous charts taking the reader through the thinking behind the trade and the process involved in managing the trade.

Readers should not be fooled by the title, which, by the way, is a double entendre. This book is not just for the layman, experienced traders will gain much from the book. But the completely inexperienced trading novice will walk away with a comprehensive trading strategy that will help them avoid the next bear market that could wipe out what’s left of their retirement
accounts.

This book was published by Stilwell & Company Publishing Group and released in October 2010.

How would you describe your job?

I trade my own personal accounts and have a retail and institutional consulting business. My 11 year old daughter described it the best: “My dad looks at charts all day.”

What led you to look at the particular markets you specialize in as opposed to another tradable?

I trade mostly stocks but I will trade other markets. Less efficient markets tend to trend better than well watched markets. Those more efficient markets are a crowded playing field. Participants often cancel each other out. This makes for choppy trading. Someone’s not going to wake up tomorrow and say, hey, there’s this thing called the Emini or the EUR/USD. Somewhat less liquid stocks (within reason) can have some really nice trends as they are discovered. This is not to say that I won’t ever trade a market like Forex. You just have to pick your spots carefully. In more efficient markets I only trade transitional type of patterns (early trend) off of major highs or lows. Some big trends can be caught doing this. However, these don’t come along every day. So, you have to be patient.

Do you look at any fundamental or economic inputs to develop your opinions?

I ignore all news and all fundamentals. I haven’t had a TV in my office in years.

What technique do you rely on the most? Can you describe this tool?

I’m a trend follower who trades pullbacks and variations thereof. Most of the time I use blank charts (no indicators) and just “eyeball” the trend. I will occasionally use what I have dubbed “Bowtie” moving averages (10SMA,20EMA,30EMA), especially when a market is making some sort of distribution type of top or bottom. Again though, most of the time I just use blank charts. On the back of my business cards I have 3 arrows. One pointed up with the word “Uptrend”, one pointing down with the word “Downtrend”, and one pointing sideways labeled “No Trend (sideways). You’d be surprised how these 3 arrows can keep you on the right side or out of a market. This serves as a constant reminder. If any of your readers would like this wondering trend finding tool, send a self addressed, stamped envelope to Sentive Trading, LLC P.O. Box 298 Abita Springs, LA 70420 and I’ll mail them one.

What advice would you have for someone starting in the business today?

I occasionally will do a presentation where I add more and more indicators until the price chart completely disappears. Then, I peel off all these layers to show that the answer was in the blank chart all along. From what I’ve seen, we all have to go through this “grail hunt” searching for the perfect indicator. The reality is, there is none. So my advice would be to keep it simple. Find one pattern and get good at it (vs. spending years searching). I often preach, if you’re not profitable with one pattern, what makes you think you’re going to be profitable trading 10?

Money management is important. As I often say, “money management will cure a multitude of sins.” It will keep you in the game when conditions are less-than-ideal and it will keep you from letting things go to your head in great conditions.

This leads us to my next point, trading psychology. Realize that the battle is often from within. I’ve never met an unsuccessful paper trader. Once real money is put on the line, things begin to change. Also, the trading world is much different than the real world. If you’re smart, it’s going to take longer. Doctors and engineers tend to make the worst traders since their careers were approached with a high degree of logic. In trading, often there is none. Markets trade on emotions, period. You’re going to be wrong a lot. Get used to it. If half of your bridges fell or patients died, you wouldn’t be in business long. However, in trading, you can be wrong more than half of the time and still be profitable. Give yourself some time. You didn’t become a doctor, lawyer, or automatic transmission mechanic overnight. You’ll have to experience a variety of conditions. You can read about bull and bear cycles, but until you experience the euphoria or the fear, you won’t know how you will react.

So, start small and slowly add. Again, learn one pattern and get good at it. I often recommend “Persistent Pullbacks” for those just starting out (email me and I’ll give you the pattern). This simple pattern will keep you on the right side of the market and out of the market completely when the market is going sideways. For instance, in 2008, there were numerous sell signals and no buy signals. Also, remember you’ll only be smarter in the future. Longevity is key. Trade at a size that seems almost meaningless until you have experienced a variety of conditions.

Trend-following methods have been successfully utilized by generations of traders to build attractive return streams while reducing risk of catastrophic outcomes. Jesse Livermore, George Soros, John W.
Henry and Ed Seykota are just a few of the luminary traders that have used trend-following strategies to make millions (or billions) during a variety of economic environments. During the last decade, no-one has worked harder to bring these trading techniques into the public light than author Michael Covel.

Covel’s initial goal was to be a trader, and he started by asking a very simple question: “Who were the biggest market winners, and what were they doing differently that allowed them to succeed in an arena littered with failure?” His research eventually led him to trend-following; Covel became so enmeshed in his research that he decided to shift his focus from becoming a trader to helping the public learn about the methods that he had unearthed.

The latest addition to Wiley’s “The Little Book” investment series comes from Covel and focuses on the topic of trading. More specifically, the book deals with “Trend Following Trading”, a subject that Covel wrote the book on, literally (Trend Following: Learn to Make Millions in Up or Down Markets). In this tranche of the “Little Book” series, Covel introduces the reader to the trend following methods that accomplished traders have been using for generations to earn substantial profits. The book mixes the educational material with profiles of a select group of eleven traders, some of whom have not been discussed in Covel’s earlier books.

Each of the book’s chapters tells the story of a specific trader and how they came to use trend following techniques to generate profits. The chapters are also filled with a variety of supporting material and commentary provided by Covel to help the reader to better understand trend following methods, their uses, and their abuses. The traders profiled are legendary: Jack Forrest, David Druz, Paul Mulvaney, and David Harding among others.

In the stories, several common themes emerge. Most prominent is the fact that many of the successful traders started as fundamentally driven investors. Initially experiencing failure, the protagonists migrated towards a technical/systematic approach to trading resulting in spectacular profits. Another theme is that many (if not all) of the traders are self-made, having established their track records far from Wall Street investment banks. This characteristic is of particular interest since the target market for the “Little Book” series is the retail investor.

On one level, the book digs deeply into the successful strategies employed by some of the best investment professionals. On another level, the book gives hope to the average investor that there is a better way forward that doesn’t involve a string of PhD’s, banks of computers, and endless pots of money. The message is that a small amount of capital, a large amount of discipline, and a trend-following method can set anyone on the path towards prosperity.

In the September issue of Technically Speaking, we presented a case study where a CMT candidate stumbled upon information about a pending takeover while working in a part-time job that was unrelated to any professional investing activity. Our conclusion was that the candidate could not act on the information without violating the MTA Code of Ethics.

A reader offered the following rebuttal:

“I do not agree with your answer to the Ethics Corner question about the CMT candidate who came by a draft press release while engaged in cleaning an office.

1. 1. 1. The protagonist did not know whether the draft press release reflected the current thinking of the investment bank. If the bank changed its mind, the discarded press release would be of no use. 
      2. It is true that CMTs are required to be scrupulously above suspicion regarding non-public information. But then, so are bankers. If the banker was careless enough to not shred the press release, it is he who is in violation of the bank’s rules.  The CMT candidate did not induce such behavior from the banker. The banker was presumably in full control of his faculties and his office when he chose to trash the papers, knowing fully well that they were accessible to cleaning
         staff etc.
      3. If the papers are found in the clear, why should the CMT candidate not assume that material was already in the process of disclosure?

Why should a CMT candidate be penalized for benefiting from the carelessness of others?”

This was not an anonymous response, but we have chosen to withhold the author’s name. They certainly present a strong opinion, and it is one that we felt could be detrimental to future employment opportunities because a company may not be willing to hire someone who takes this view. In the interest of encouraging debate on ethical points, we will always err on the side of anonymity and allow others to express opinions without creating a long-standing record of their views. This also allows us to highlight that internet postings will always be available to potential employers, and this includes even personal pages on social media sites in many cases. Professionals should always ensure that their internet presence reflects the image they would choose to project in all cases.

The first point raised by the reader’s response is whether or not the information is current and valuable. If the CMT applicant did not believe that it was, he would probably not act on the tip by buying the stock in question. This makes the argument raised dubious. It could serve as the justification for a losing trade but in no way does it seem to be an acceptable rationale to support the argument that the CMT candidate had no way of knowing whether or not the information is valuable. Traders tend only to act on information that they consider to be valuable by the very definition of trading.

In the second point, the reader shifts responsibility to the careless banker. This is unlikely to be a winning legal argument, and it is also not in keeping with the spirit of a Code of Ethics. By adopting the Code, the MTA created a voluntary set of guidelines which are stricter than the law and members and affiliates agree to act in a manner that is above the minimum legal requirements. With the privilege of using the CMT, there is also a degree of personal responsibility for one’s analysis and professional behavior.

In this case, it would not be acceptable to assume the information was disclosed without checking. The CMT candidate can easily verify that the information is in the public domain with a quick search of news related to the company. He or she can not first act and then claim no way of knowing that the information was private given the availability of free news services to verify the information.

The last question in the reader’s response shows why we have a concern about future employment opportunities if we publish their name. Many firms will want to employ only those who are willing to meet the highest ethical standards. They will want to avoid hiring those who can justify behavior that could be a problem. In ethics, there may very well be questions which fall into a grey zone and arguments could easily be made that such an action is ethical or unethical. The problem confronting firms in these situations is that making the argument to defend an action to regulators or other authorities can hurt profitability by taking time away from other activities. It can also hurt business by demonstrating the firm is willing to engage in questionable behavior to book a small profit.

Insider trading in the United States is defined in rules issued by the Securities and Exchange Commission and a number of court rulings. They are summarized at http://go.mta.org/147. At that page, readers will also see that the European Community and a number of other nations have also defined the nature of insider trading. The rules extend beyond officers and directors of a company to include anyone with material information that they can use to create an unfair advantage in the market.

Within the MTA Code of Ethics, Standard 5 states that, “Members and Affiliates shall not seek, disseminate or act on the basis of material, nonpublic (inside) information, if to do so would violate the laws and regulations of any government, governmental agency or regulatory organization relating to the use of inside information.”

By virtue of his education, the candidate in this scenario would surely understand the information he had seen was material. It is true that this assumption may not apply to all persons working for the cleaning company, but it would be difficult to argue that someone able to use options  strategies would not understand what material information consists of. We still believe that the fact that it was not released to the public makes it inside information and the candidate should not have acted on it.

While there does not seem to be a conflict between the laws and the Code of Ethics in this case, we would like to take the opportunity to point out that if such a conflict does exist, the MTA member or affiliate needs to follow the stricter of the two.

Editor’s note: This article is a detailed look at an indicator traditionally used in fundamental analysis. It is also an exploration of an important part of technical analysis. As a measure of excess returns, beta is actually a measure of relative strength and is therefore also a technical tool. This article demonstrates that the tools fundamental analysts use are no more stable than traditional technical indicators and an analyst needs to apply judgment to any indicator within a defined framework. It also demonstrates that beta, and relative strength, vary over time. This shows that portfolio selection is a dynamic process.

There is more math in this paper than seen in the typical article that appears in this newsletter. Technicians may want to consider applying more mathematical logic to the tools they use, a nontraditional approach to technical analysis but one that does have some merit.

We have also removed most of the tables and figures in recognition of the fact that our audience does not generally read this type of paper. While it makes for easier reading, we believe the original intent and exhaustive degree of research can still be seen. The original article with all tables and figures can be seen online at http://go.mta.org/153. The points of the paper are still clear, as is the conclusion.

ABSTRACT: We compute the correlations of the annual stock returns (1989- 2008) of the Dow Jones companies with a) β Rm; and with b) Rm; and find that the second correlation (assuming beta = 1 for all companies) is higher than the first one, on average, and for all companies except Caterpillar and General Motors. Rm is the return of the S&P 500. Beta = 1 works better than calculated betas!

Not surprisingly, Adjusted betas (0.67 calculated beta + 0.33) have higher correlation than calculated betas. But Adjusted betas have lower correlation than beta = 1.

We do the exercise with 4 calculated betas every year end vs. the S&P 500, using: a) monthly data of last 5 years; b) monthly data of last 2 years; c) weekly data of last 5 years; d) daily data of last 5 years. We find similar results with the four betas.

Despite this results, Fernandez (2009) reports that 97.3 % of the professors that justify the betas use regressions, webs, databases, textbooks or papers (the paper specifies which ones), although many of them admit that calculated betas “are poorly measured and have many problems”. Only 0.9% of the professors justified the beta using exclusively personal judgment.

We compute the correlations of the annual stock returns (1989-2008) of the Dow Jones companies with a) β Rm; and with b) Rm and find that the second correlation (assuming beta = 1 for all companies) is higher than the first one on average and for all companies except Caterpillar and General Motors. Rm is the return of the S&P 500. Beta = 1 works better than calculated betas!

Not surprisingly, Adjusted betas (0.67 calculated beta + 0.33) have higher correlation than calculated betas. But Adjusted betas have lower correlation than beta = 1.

We do the exercise with 4 calculated betas every year end vs. the S&P 500, using: a) monthly data of last 5 years; b) monthly data of last 2 years; c) weekly data of last 5 years; d) daily data of last 5 years.

Table 1 shows the Correlation (Rt, β Rmt), the Correlation (Rt, Rmt) and their difference using 4 different calculated betas: a) monthly data of the last 5 years; b) monthly data of the last 2 years; c) weekly data of the last 5 years; d) daily data of the last 5 years. The betas are calculated every year end vs. the S&P 500 using Datastream. It may be seen that 21 companies had Correlation (Rt, Rmt) > Correlation (Rt, β Rmt) using betas calculated with monthly data of the last 5 years. With the other calculated betas, 23, 24 and 17 companies had this result. It also may be seen that the average Correlation (Rt, β Rmt) – Correlation (Rt, Rmt) is negative in the 4 cases. Only Caterpillar and General Motors have the four differences > 0.

Table 1. 1989 -2008. Raw betas vs. BETA =1. Correlation (Rt, β Rmt), Correlation (Rt, Rmt) and its difference using 4 different calculated betas: a) monthly data of last 5 years; b) monthly data of last 2 years; c) weekly data of last 5 years; d) daily data of last 5 years. Betas calculated every year end vs. the S&P 500. Source: Datastream

Table 2 shows the Correlation (Rt, ADJβ Rmt), the Correlation (Rt, Rmt) and their difference using the 4 different calculated betas of Table 1. ADJβ = Adjusted beta = 0,67 Raw beta + 0,33. It may be seen that 19 companies had Correlation (Rt, Rmt) > Correlation (Rt, ADJβ Rmt) using adjusted betas calculated with monthly data of the last 5 years. With the other adjusted betas, 20, 22 and 15 companies had this result. It also may be seen that the average Correlation (Rt, ADJβ Rmt) – Correlation (Rt, Rmt) is negative in the 4 cases. Only Caterpillar, Chevron and General Motors have the four differences > 0.

Editor’s note: In the remainder of this paper, a significant number of tables are referenced and they are included in the original paper. They have been omitted here but all of the text is exactly as it appears in the original paper.

Not surprisingly, adjusted betas (0.67 Raw beta + 0.33) work better than Raw betas. Table 3 shows the Correlation (Rt, β Rmt), the Correlation (Rt, ADJβ Rmt) and their difference using the 4 different calculated betas of Table 1. ADJβ = Adjusted beta = 0,67 Raw beta + 0,33. It may be seen that 25 companies had Correlation (Rt, ADJβ Rmt) > Correlation (Rt, β Rmt) using adjusted betas calculated with monthly data of the last 5 years. With the other adjusted betas, 24, 24 and 19 companies had this result. It also may be seen that the average Correlation (Rt, β Rmt) – Correlation (Rt, ADJβ Rmt) is negative in the 4 cases. It may be seen that the average difference is higher (in absolute value) in table 1 than in table 3. As in table 1, only Caterpillar and General Motors have the four differences > 0.

A) BETAS calculated vs. the S&P 500 at year end using MONTHLY data of the last 5 YEARS

Figure 1. BETA calculated vs. the S&P 500 at year end using MONTHLY data of the last 5 YEARS

Table 4 shows the difference between 2 Correlations [β (Rt, β Rmt) – β (Rt, Rmt) from 1989 to the indicated year]. It may be seen that the average difference is negative in the 11 intervals considered.

Table 5 shows the difference between 2 Correlations [β (Rt, β Rmt) – β (Rt, Rmt) from the indicated year until 2008]. It may be seen that the average difference is negative in the 11 intervals considered.

B) BETAS calculated vs. the S&P 500 at year end using MONTHLY data of the last 2 YEARS

Table 6 shows the difference between 2 Correlations [β (Rt, β Rmt) –  (Rt, Rmt) from 1989 to the indicated year]. It may be seen that the average difference is negative in the 11 intervals considered.

Table 7 shows the difference between 2 Correlations [β (Rt, β Rmt) –  (Rt, Rmt) from the indicated year until 2008]. It may be seen that the average difference is negative in the 11 intervals considered.

C) BETAS calculated vs. the S&P 500 at year end using WEEKLY data of the last 5 YEARS

Figure 3 shows the evolution of the BETA of each company calculated vs. the S&P 500 at year end using WEEKLY data of the last 5 YEARS

Table 8 shows the difference between 2 Correlations [β (Rt, β Rmt) – β (Rt, Rmt) from 1989 to the indicated year]. It may be seen that the average difference is negative in the 11 intervals considered.

Table 9 shows the difference between 2 Correlations [β (Rt, β Rmt) – β (Rt, Rmt) from the indicated year until 2008]. It may be seen that the average difference is negative in the 11 intervals considered.

D) BETAS calculated vs. the S&P 500 at year end using DAILY data of the last 5 YEARS

Figure 4 shows the evolution of the BETA of each company calculated vs. the S&P 500 at year end using DAILY data of the last 5 YEARS

Table 10 shows the difference between 2 Correlations [β (Rt, β Rmt) – β (Rt, Rmt) from 1989 to the indicated year]. It may be seen that the average difference is negative in the 11 intervals considered.

Table 11 shows the difference between 2 Correlations [β (Rt, β Rmt) – β (Rt, Rmt) from the indicated year until 2008]. It may be seen that the average difference is negative in the 11 intervals considered.

Table 12 contains the last column of tables 4 to 11. It may be seen, again, that β = 1 has higher correlation with returns than calculated betas for all companies except Caterpillar and General Motors

Despite this results, Fernandez (2009)1 reports that 97.3 % of the professors that justify the betas use regressions, webs, databases, textbooks or papers (the paper specifies which ones), although many of them admit that calculated betas “are poorly measured and have many problems”. Only 0.9% of the professors justified the beta using exclusively personal judgment (named qualitative betas, common sense betas, intuitive betas, logical magnitude betas and own judgment betas by different professors).

Fernandez, P. (2009), “Betas used by Professors: a survey with 2,500 answers”, SSRN n. 1407464.

This paper was originally published at http://go.mta.org/153 and is extracted here with the permission of the author.

The CMT Board of Governors (BoG) is pleased to announce the creation of the full time position of Director, CMT Studies. The immediate responsibility of this position is the development and integration of CMT study materials, including Learning Outcome Statements and a customized curriculum for our candidates. This full time staff member will also participate in coordinating the work of Subject Matter Experts in the preparation of CMT exams. This position will report to the Executive Director of the MTA, with a strong working relationship to the Chairperson of the BoG and the BoG itself.

The ideal candidate will have, as a minimum, a graduate level degree and at least 5 years of relevant teaching experience, with an emphasis on distance learning. They will have a strong, comprehensive knowledge of the financial markets, with the Chartered Market Technician (CMT) designation preferred, but not required. This individual must have excellent verbal and written communication skills; exceptional project management skills; excellent team building skills and ability to work, often remotely, in a team environment.

Interested individuals should submit their resumes with references to [email protected] by October 21, 2011.

Complete job description is listed to the below.

Title: Director, CMT Studies
Location: MTA Global Headquarters, New York City, New York
Salary and benefits: Competitive
Travel is required.
Job Requirements:

• Graduate degree required.
• CMT preferred, but not required. Must have strong, comprehensive knowledge of financial markets.
• At least five-years of teaching experience, with emphasis on distance learning, including exam construction, curriculum
  development and assessment.
• Excellent verbal and written communication skills.
• Exceptional project management skills.
• Excellent team building skills and ability to work in a team environment.

Responsibilities:

• Coordinating Subject Matter Experts in the preparation of CMT exams and preparation materials.
• Developing CMT study materials, including Learning Outcome Statements.
• Developing and distributing CMT customized curriculum.
• Developing and distributing CMT exam preparation materials, such as practice exams.